In this paper a fuzzy logic-based hierarchical multilevel risk calculation model will be introduced with different model parameters. On each occasion when a fuzzy-based simulation model is constructed, the appropriate aggregation and defuzzification method must be chosen. It is very difficult because it cannot be said generally, which is the best method, its depends on the current application. The model presented in the paper is a model for risk calculation of physical exercise, and it was constructed in Simulink - Fuzzy Logic Toolbox environment with Mamdani-type fuzzy evaluation and different aggregation and defuzzification operators. The test was performed for several typical groups of the patients. The results are compared with a previously implemented Analytic Hierarchy Process with Fuzzy Comprehensive Evaluation based model with similar purposes. The result of the comparison has been analyzed and the best methods have been selected.

Drone technology has undoubtedly become an integral part of our everyday life in recent years. The business and industrial use of unmanned aerial vehicles (UAVs) can provide advantageous solutions in many areas of life, and they are also optimal for emergency situations and for accessing hard-to-reach places. However, their application poses numerous technological and regulatory challenges to be overcome. One of the weak links in the operation of UAVs is the limited availability of energy. In order to address this issue, the authors developed a novel trajectory planning method for UAVs to optimize energy consumption during flight. First, an “energy map” was created, which was the basis for trajectory planning, i.e., determining the energy consumption of the individual components. This was followed by configuring the 3D environment including partitioning of the work space (WS), i.e., defining the free spaces, occupied spaces (obstacles), and semi-occupied/free spaces. Then, the corresponding graph-like path(s) were generated on the basis of the partitioned space, where a graph search-based heuristic trajectory planning was initiated, taking into account the most important wind conditions including velocity and direction. Finally, in order to test the theoretical results, some sample environments were created to test and analyze the proposed path generations. The method eventually proposed was able to determine the optimal path in terms of energy consumption.

In this paper two types of hierarchical multilevel models will be introduced for the calculation of risk of physical exercise in fuzzy environment. One of them is the Analytic Hierarchy Process with Fuzzy Comprehensive Evaluation based model and the other was made in Simulink-Fuzzy Toolbox environment with Mamdani-type fuzzy evaluation. The two methods were tested on several input data and the result of them has been compared and analyzed.

In this paper a possible simplification of a risk level calculation model's neuro-fuzzy subsystem will be studied. The basic model has a hierarchical multilevel structure and uses fuzzy logic based decision making for some groups of risk factors and neuro-fuzzy subsystem is based on ANFIS model structure for the other. The simplification of neuro-fuzzy subsystem is based on disjunction fuzzy operator where the operator selection is application-dependent. This is because the three basic operations of crisp sets (negation, conjunction, and disjunction) can be generalized for fuzzy sets in an infinite number of ways. In this work the effect of different conjunction and disjunction operators on the result of simplified structure was compared and analyzed.

Nowadays, the changes in people's habits and the development of technology result in the wide spreading of the model-based health monitoring systems. In this paper, an improvement of the measurement evaluation method is introduced, which is used during the sport activity in real-time. The basis of the novel approach is our previously reported anytime hierarchical fuzzy risk calculation model, which is able to handle some uncertainties, imprecision, and subjectivity in the data and in the evaluation process and can cope with the dynamically changing environment, available time, and resources. In the new model the input membership functions, which are tuned according to the patient characteristics, are modified based on the data recorded during previous measurements under the same conditions. By this, the person-dependent characteristics and the unavoidable changing of the dynamic reactions of the human organism can also be considered and the risk level can be more reliably predicted.

It is necessary to extensively investigate the causes of road accidents with the utmost precision to harness future technological advancements, such as autonomous driving and intelligent accident prevention systems. Nevertheless, since most accidents are attributed to simple human errors, unraveling the complex root-cause factors poses a considerable challenge. This is where fuzzy logic can offer a potential solution: it is essential to understand even seemingly straightforward errors, such as speeding, to identify external factors that could play a pivotal role in future accident prevention. A more in-depth examination and comprehension of elements like road curvature, slope, and their correlation with accidents are necessary. Additionally, it is crucial to explore how the frequency of accidents on specific road segments varies under diverse weather conditions. This article analyzes which curves can be considered more dangerous and the factors that render them risky. The fuzzy model presented in this article is primarily capable of estimating the risk of a given road segment based on its curvature characteristics. The model results presented in the article indicate that sections of the road can become more risky due to multiple curves and curves with a radius of less than 80 m. The model assesses risk based on the physical characteristics of road segments, primarily the curvature radius, while, typically, other road risk assessment models rely on traffic volume and accident counts.